Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment

The NEXT collaboration

Research output: Contribution to journalArticlepeer-review

Abstract

Convolutional neural networks (CNNs) are widely used state-of-the-art computer vision tools that are becoming increasingly popular in high-energy physics. In this paper, we attempt to understand the potential of CNNs for event classification in the NEXT experiment, which will search for neutrinoless double-beta decay in 136Xe. To do so, we demonstrate the usage of CNNs for the identification of electron-positron pair production events, which exhibit a topology similar to that of a neutrinoless double-beta decay event. These events were produced in the NEXT-White high-pressure xenon TPC using 2.6 MeV gamma rays from a 228Th calibration source. We train a network on Monte Carlo-simulated events and show that, by applying on-the-fly data augmentation, the network can be made robust against differences between simulation and data. The use of CNNs offers significant improvement in signal efficiency and background rejection when compared to previous non-CNN-based analyses. [Figure not available: see fulltext.]

Original languageEnglish (US)
Article number189
JournalJournal of High Energy Physics
Volume2021
Issue number1
DOIs
StatePublished - Jan 2021

Keywords

  • Dark Matter and Double Beta Decay (experiments)

ASJC Scopus subject areas

  • Nuclear and High Energy Physics

Fingerprint Dive into the research topics of 'Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment'. Together they form a unique fingerprint.

Cite this